Overview

Dataset statistics

Number of variables9
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory78.8 B

Variable types

Categorical4
Text2
Numeric3

Dataset

Description경상남도 양산시 습지 지역 현황에 대한 데이터로 습지유형, 구분, 주소, 위도, 경도, 면적(m²), 자연성 등의 항목을 제공합니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15074087

Alerts

출처 has constant value ""Constant
위도 is highly overall correlated with 데이터기준일자High correlation
경도 is highly overall correlated with 습지유형High correlation
습지유형 is highly overall correlated with 경도High correlation
데이터기준일자 is highly overall correlated with 위도High correlation
데이터기준일자 is highly imbalanced (81.3%)Imbalance
구분 has unique valuesUnique
면적 has 8 (22.9%) zerosZeros

Reproduction

Analysis started2023-12-11 00:00:01.709984
Analysis finished2023-12-11 00:00:03.330713
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

습지유형
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
산지습지
24 
하천습지
10 
배후습지
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row산지습지
2nd row산지습지
3rd row산지습지
4th row산지습지
5th row산지습지

Common Values

ValueCountFrequency (%)
산지습지 24
68.6%
하천습지 10
28.6%
배후습지 1
 
2.9%

Length

2023-12-11T09:00:03.432070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:00:03.546042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산지습지 24
68.6%
하천습지 10
28.6%
배후습지 1
 
2.9%

구분
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T09:00:03.734397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.6
Min length4

Characters and Unicode

Total characters231
Distinct characters72
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row영축산 단조늪
2nd row영축산 통도사습지
3rd row영축산 재늪
4th row영축산 운초늪
5th row영축산 한피기습지숲
ValueCountFrequency (%)
천성산 8
 
13.8%
솥발산 7
 
12.1%
영축산 5
 
8.6%
가산습지 1
 
1.7%
황산습지 1
 
1.7%
대성큰늪 1
 
1.7%
양산습지 1
 
1.7%
와곡습지 1
 
1.7%
대성뒷늪 1
 
1.7%
백록늪 1
 
1.7%
Other values (31) 31
53.4%
2023-12-11T09:00:04.152247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 28
 
12.1%
23
 
10.0%
늪 18
 
7.8%
지 16
 
6.9%
습 15
 
6.5%
성 12
 
5.2%
천 8
 
3.5%
발 7
 
3.0%
솥 7
 
3.0%
축 5
 
2.2%
Other values (62) 92
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 208
90.0%
Space Separator 23
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 28
 
13.5%
늪 18
 
8.7%
지 16
 
7.7%
습 15
 
7.2%
성 12
 
5.8%
천 8
 
3.8%
발 7
 
3.4%
솥 7
 
3.4%
축 5
 
2.4%
적 5
 
2.4%
Other values (61) 87
41.8%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
90.0%
Common 23
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 28
 
13.5%
늪 18
 
8.7%
지 16
 
7.7%
습 15
 
7.2%
성 12
 
5.8%
천 8
 
3.8%
발 7
 
3.4%
솥 7
 
3.4%
축 5
 
2.4%
적 5
 
2.4%
Other values (61) 87
41.8%
Common
ValueCountFrequency (%)
23
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
90.0%
ASCII 23
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 28
 
13.5%
늪 18
 
8.7%
지 16
 
7.7%
습 15
 
7.2%
성 12
 
5.8%
천 8
 
3.8%
발 7
 
3.4%
솥 7
 
3.4%
축 5
 
2.4%
적 5
 
2.4%
Other values (61) 87
41.8%
ASCII
ValueCountFrequency (%)
23
100.0%

주소
Text

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T09:00:04.323549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length15.657143
Min length12

Characters and Unicode

Total characters548
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)45.7%

Sample

1st row경상남도 양산시 원동면 선리 1-1
2nd row경상남도 양산시 하북면 지산리
3rd row경상남도 양산시 하북면 지산리
4th row경상남도 양산시 하북면 지산리
5th row경상남도 양산시 원동면 선리
ValueCountFrequency (%)
경상남도 35
25.2%
양산시 35
25.2%
하북면 19
13.7%
용연리 11
 
7.9%
동면 5
 
3.6%
원동면 5
 
3.6%
상북면 3
 
2.2%
백록리 3
 
2.2%
지산리 3
 
2.2%
금산리 2
 
1.4%
Other values (17) 18
12.9%
2023-12-11T09:00:04.631779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
19.0%
산 42
 
7.7%
상 39
 
7.1%
경 35
 
6.4%
남 35
 
6.4%
도 35
 
6.4%
양 35
 
6.4%
시 35
 
6.4%
면 32
 
5.8%
리 32
 
5.8%
Other values (35) 124
22.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 441
80.5%
Space Separator 104
 
19.0%
Decimal Number 2
 
0.4%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 42
9.5%
상 39
 
8.8%
경 35
 
7.9%
남 35
 
7.9%
도 35
 
7.9%
양 35
 
7.9%
시 35
 
7.9%
면 32
 
7.3%
리 32
 
7.3%
북 22
 
5.0%
Other values (32) 99
22.4%
Space Separator
ValueCountFrequency (%)
104
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 441
80.5%
Common 107
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 42
9.5%
상 39
 
8.8%
경 35
 
7.9%
남 35
 
7.9%
도 35
 
7.9%
양 35
 
7.9%
시 35
 
7.9%
면 32
 
7.3%
리 32
 
7.3%
북 22
 
5.0%
Other values (32) 99
22.4%
Common
ValueCountFrequency (%)
104
97.2%
1 2
 
1.9%
- 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 441
80.5%
ASCII 107
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
97.2%
1 2
 
1.9%
- 1
 
0.9%
Hangul
ValueCountFrequency (%)
산 42
9.5%
상 39
 
8.8%
경 35
 
7.9%
남 35
 
7.9%
도 35
 
7.9%
양 35
 
7.9%
시 35
 
7.9%
면 32
 
7.3%
리 32
 
7.3%
북 22
 
5.0%
Other values (32) 99
22.4%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.417422
Minimum35.28543
Maximum35.523932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:00:04.754074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.28543
5-th percentile35.300553
Q135.388077
median35.434851
Q335.458464
95-th percentile35.494535
Maximum35.523932
Range0.238502
Interquartile range (IQR)0.0703865

Descriptive statistics

Standard deviation0.062094749
Coefficient of variation (CV)0.0017532261
Kurtosis-0.37192759
Mean35.417422
Median Absolute Deviation (MAD)0.034696
Skewness-0.64034662
Sum1239.6098
Variance0.0038557578
MonotonicityNot monotonic
2023-12-11T09:00:04.856813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
35.434851 11
31.4%
35.461426 3
 
8.6%
35.490237 3
 
8.6%
35.300553 2
 
5.7%
35.523932 1
 
2.9%
35.388887 1
 
2.9%
35.28543 1
 
2.9%
35.320816 1
 
2.9%
35.469547 1
 
2.9%
35.453539 1
 
2.9%
Other values (10) 10
28.6%
ValueCountFrequency (%)
35.28543 1
2.9%
35.300553 2
5.7%
35.318721 1
2.9%
35.320816 1
2.9%
35.335046 1
2.9%
35.350974 1
2.9%
35.357053 1
2.9%
35.387267 1
2.9%
35.388887 1
2.9%
35.393211 1
2.9%
ValueCountFrequency (%)
35.523932 1
 
2.9%
35.504565 1
 
2.9%
35.490237 3
 
8.6%
35.469547 1
 
2.9%
35.461426 3
 
8.6%
35.455501 1
 
2.9%
35.453539 1
 
2.9%
35.434851 11
31.4%
35.430515 1
 
2.9%
35.395298 1
 
2.9%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.06305
Minimum128.89707
Maximum129.10865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:00:04.951003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.89707
5-th percentile128.9803
Q1129.03821
median129.07223
Q3129.10865
95-th percentile129.10865
Maximum129.10865
Range0.211586
Interquartile range (IQR)0.070443

Descriptive statistics

Standard deviation0.048831822
Coefficient of variation (CV)0.00037835633
Kurtosis2.4794239
Mean129.06305
Median Absolute Deviation (MAD)0.036421
Skewness-1.373244
Sum4517.2068
Variance0.0023845468
MonotonicityNot monotonic
2023-12-11T09:00:05.050630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
129.108652 11
31.4%
129.092469 3
 
8.6%
129.050352 3
 
8.6%
129.038209 2
 
5.7%
129.018464 1
 
2.9%
129.044425 1
 
2.9%
129.028419 1
 
2.9%
129.072231 1
 
2.9%
129.07235 1
 
2.9%
129.064173 1
 
2.9%
Other values (10) 10
28.6%
ValueCountFrequency (%)
128.897066 1
2.9%
128.979375 1
2.9%
128.980694 1
2.9%
129.005246 1
2.9%
129.005858 1
2.9%
129.018464 1
2.9%
129.028419 1
2.9%
129.037418 1
2.9%
129.038209 2
5.7%
129.044425 1
2.9%
ValueCountFrequency (%)
129.108652 11
31.4%
129.095825 1
 
2.9%
129.092469 3
 
8.6%
129.081499 1
 
2.9%
129.07235 1
 
2.9%
129.072231 1
 
2.9%
129.07072 1
 
2.9%
129.064173 1
 
2.9%
129.052958 1
 
2.9%
129.050352 3
 
8.6%

면적
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120885.34
Minimum0
Maximum1870000
Zeros8
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:00:05.162222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11463.5
median12982
Q385326
95-th percentile458639.1
Maximum1870000
Range1870000
Interquartile range (IQR)83862.5

Descriptive statistics

Standard deviation326834.3
Coefficient of variation (CV)2.7036719
Kurtosis25.597434
Mean120885.34
Median Absolute Deviation (MAD)12982
Skewness4.823727
Sum4230987
Variance1.0682066 × 1011
MonotonicityNot monotonic
2023-12-11T09:00:05.275328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 8
22.9%
207160 2
 
5.7%
9000 2
 
5.7%
37500 1
 
2.9%
1870000 1
 
2.9%
137953 1
 
2.9%
26855 1
 
2.9%
172916 1
 
2.9%
50925 1
 
2.9%
27000 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
0 8
22.9%
1127 1
 
2.9%
1800 1
 
2.9%
2500 1
 
2.9%
4300 1
 
2.9%
6856 1
 
2.9%
7500 1
 
2.9%
8923 1
 
2.9%
9000 2
 
5.7%
12982 1
 
2.9%
ValueCountFrequency (%)
1870000 1
2.9%
464545 1
2.9%
456108 1
2.9%
250000 1
2.9%
207160 2
5.7%
172916 1
2.9%
137953 1
2.9%
119727 1
2.9%
50925 1
2.9%
45000 1
2.9%

자연성
Categorical

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
상
21 
하(개간)
4 
산지화
4 
중
4 
하
 
2

Length

Max length5
Median length1
Mean length1.6857143
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상
2nd row하(개간)
3rd row상
4th row상
5th row상

Common Values

ValueCountFrequency (%)
상 21
60.0%
하(개간) 4
 
11.4%
산지화 4
 
11.4%
중 4
 
11.4%
하 2
 
5.7%

Length

2023-12-11T09:00:05.427162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:00:05.538622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상 21
60.0%
하(개간 4
 
11.4%
산지화 4
 
11.4%
중 4
 
11.4%
하 2
 
5.7%

출처
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
기본현황
35 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기본현황
2nd row기본현황
3rd row기본현황
4th row기본현황
5th row기본현황

Common Values

ValueCountFrequency (%)
기본현황 35
100.0%

Length

2023-12-11T09:00:05.645813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:00:05.743514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본현황 35
100.0%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
2020-06-31
34 
2021-08-09
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row2020-06-31
2nd row2020-06-31
3rd row2020-06-31
4th row2020-06-31
5th row2021-08-09

Common Values

ValueCountFrequency (%)
2020-06-31 34
97.1%
2021-08-09 1
 
2.9%

Length

2023-12-11T09:00:05.852176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:00:05.961496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-31 34
97.1%
2021-08-09 1
 
2.9%

Interactions

2023-12-11T09:00:02.765033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.147007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.435651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.882035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.225020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.544372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.985256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.326846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:00:02.644542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:00:06.424127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
습지유형구분주소위도경도면적자연성데이터기준일자
습지유형1.0001.0000.9820.5130.9010.4670.1370.000
구분1.0001.0001.0001.0001.0001.0001.0001.000
주소0.9821.0001.0001.0001.0000.9900.0001.000
위도0.5131.0001.0001.0000.8830.4780.2940.573
경도0.9011.0001.0000.8831.0000.5500.0000.607
면적0.4671.0000.9900.4780.5501.0000.0000.000
자연성0.1371.0000.0000.2940.0000.0001.0000.000
데이터기준일자0.0001.0001.0000.5730.6070.0000.0001.000
2023-12-11T09:00:06.562086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일자습지유형자연성
데이터기준일자1.0000.0000.000
습지유형0.0001.0000.081
자연성0.0000.0811.000
2023-12-11T09:00:06.670496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도면적습지유형자연성데이터기준일자
위도1.0000.125-0.0730.2230.1380.508
경도0.1251.000-0.2050.8100.0000.376
면적-0.073-0.2051.0000.4540.0000.000
습지유형0.2230.8100.4541.0000.0810.000
자연성0.1380.0000.0000.0811.0000.000
데이터기준일자0.5080.3760.0000.0000.0001.000

Missing values

2023-12-11T09:00:03.127126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:00:03.271243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

습지유형구분주소위도경도면적자연성출처데이터기준일자
0산지습지영축산 단조늪경상남도 양산시 원동면 선리 1-135.523932129.018464207160상기본현황2020-06-31
1산지습지영축산 통도사습지경상남도 양산시 하북면 지산리35.490237129.0503526856하(개간)기본현황2020-06-31
2산지습지영축산 재늪경상남도 양산시 하북면 지산리35.490237129.0503520상기본현황2020-06-31
3산지습지영축산 운초늪경상남도 양산시 하북면 지산리35.490237129.0503520상기본현황2020-06-31
4산지습지영축산 한피기습지숲경상남도 양산시 원동면 선리35.504565129.0052460상기본현황2021-08-09
5산지습지선암산 야저늪경상남도 양산시 어곡동35.395298129.0058588923상기본현황2020-06-31
6산지습지신불산 신불산고산슾지경상남도 양산시 원동면 대리35.455501128.979375456108상기본현황2020-06-31
7산지습지금정산 금샘습원경상남도 양산시 동면 금산리35.300553129.03820912982상기본현황2020-06-31
8산지습지신지습원경상남도 양산시 동면 여락리35.318721129.0958250상기본현황2020-06-31
9산지습지천성산 화엄늪경상남도 양산시 하북면 용연리35.434851129.108652207160상기본현황2020-06-31
습지유형구분주소위도경도면적자연성출처데이터기준일자
25하천습지화제습지경상남도 양산시 원동면 화제리35.357053128.980694119727상기본현황2020-06-31
26하천습지양산습지경상남도 양산시 신기동35.350974129.052958250000상기본현황2020-06-31
27하천습지와곡습지경상남도 양산시 상북면 소토리35.388887129.04442527000상기본현황2020-06-31
28하천습지모래볼습지경상남도 양산시 상북면 대석리35.393211129.08149950925상기본현황2020-06-31
29하천습지신전습지경상남도 양산시 상북면 신전리35.430515129.07072172916중기본현황2020-06-31
30하천습지삼감습지경상남도 양산시 하북면 삼감리35.453539129.06417326855중기본현황2020-06-31
31하천습지삼수습지경상남도 양산시 하북면 삼수리35.469547129.07235137953중기본현황2020-06-31
32하천습지황산습지경상남도 양산시 동면 황산리35.320816129.0722311870000상기본현황2020-06-31
33하천습지가산습지경상남도 양산시 동면 가산리35.28543129.0284190상기본현황2020-06-31
34하천습지작은습지경상남도 양산시 동면 금산리35.300553129.0382090하기본현황2020-06-31